def recommend_best_out_of_sample_point(
model: TorchModel,
bounds: List[Tuple[float, float]],
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
model_gen_options: Optional[TConfig] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Optional[Tensor]:
"""
Identify the current best point by optimizing the posterior mean of the model.
This is "out-of-sample" because it considers un-observed designs as well.
Return None if no such point can be identified.
Args:
model: A TorchModel.
bounds: A list of (lower, upper) tuples for each column of X.
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
outcome_constraints: A tuple of (A, b). For k outcome constraints
and m outputs at f(x), A is (k x m) and b is (k x 1) such that
A f(x) <= b.
linear_constraints: A tuple of (A, b). For k linear constraints on
d-dimensional x, A is (k x d) and b is (k x 1) such that
A x <= b.
fixed_features: A map {feature_index: value} for features that
should be fixed to a particular value in the best point.
model_gen_options: A config dictionary that can contain
model-specific options.
target_fidelities: A map {feature_index: value} of fidelity feature
column indices to their respective target fidelities. Used for
multi-fidelity optimization.
Returns:
A d-array of the best point, or None if no feasible point exists.
"""
options = model_gen_options or {}
fixed_features = fixed_features or {}
acf_options = options.get("acquisition_function_kwargs", {})
optimizer_options = options.get("optimizer_kwargs", {})
X_observed = get_observed(
Xs=model.Xs, # pyre-ignore: [16]
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
if hasattr(model, "_get_best_point_acqf"):
acq_function, non_fixed_idcs = model._get_best_point_acqf( # pyre-ignore: [16]
X_observed=X_observed,
objective_weights=objective_weights,
mc_samples=acf_options.get("mc_samples", 512),
fixed_features=fixed_features,
target_fidelities=target_fidelities,
outcome_constraints=outcome_constraints,
seed_inner=acf_options.get("seed_inner", None),
qmc=acf_options.get("qmc", True),
)
else:
raise RuntimeError("The model should implement _get_best_point_acqf.")
inequality_constraints = _to_inequality_constraints(linear_constraints)
# TODO: update optimizers to handle inequality_constraints
# (including transforming constraints b/c of fixed features)
if inequality_constraints is not None:
raise UnsupportedError("Inequality constraints are not supported!")
return_best_only = optimizer_options.get("return_best_only", True)
bounds_ = torch.tensor(bounds, dtype=model.dtype, device=model.device)
bounds_ = bounds_.transpose(-1, -2)
if non_fixed_idcs is not None:
bounds_ = bounds_[..., non_fixed_idcs]
candidates, _ = optimize_acqf(
acq_function=acq_function,
bounds=bounds_,
q=1,
num_restarts=optimizer_options.get("num_restarts", 60),
raw_samples=optimizer_options.get("raw_samples", 1024),
inequality_constraints=inequality_constraints,
fixed_features=None, # handled inside the acquisition function
options={
"batch_limit": optimizer_options.get("batch_limit", 8),
"maxiter": optimizer_options.get("maxiter", 200),
"nonnegative": optimizer_options.get("nonnegative", False),
"method": "L-BFGS-B",
},
return_best_only=return_best_only,
)
rec_point = candidates.detach().cpu()
if isinstance(acq_function, FixedFeatureAcquisitionFunction):
rec_point = acq_function._construct_X_full(rec_point)
if return_best_only:
rec_point = rec_point.view(-1)
return rec_point
def test_optimize_acqf_joint(
self, mock_gen_candidates, mock_gen_batch_initial_conditions
):
q = 3
num_restarts = 2
raw_samples = 10
options = {}
mock_acq_function = MockAcquisitionFunction()
cnt = 1
for dtype in (torch.float, torch.double):
mock_gen_batch_initial_conditions.return_value = torch.zeros(
num_restarts, q, 3, device=self.device, dtype=dtype
)
base_cand = torch.ones(1, q, 3, device=self.device, dtype=dtype)
mock_candidates = torch.cat(
[i * base_cand for i in range(num_restarts)], dim=0
)
mock_acq_values = num_restarts - torch.arange(
num_restarts, device=self.device, dtype=dtype
)
mock_gen_candidates.return_value = (mock_candidates, mock_acq_values)
bounds = torch.stack(
[
torch.zeros(3, device=self.device, dtype=dtype),
4 * torch.ones(3, device=self.device, dtype=dtype),
]
)
candidates, acq_vals = optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
)
self.assertTrue(torch.equal(candidates, mock_candidates[0]))
self.assertTrue(torch.equal(acq_vals, mock_acq_values[0]))
candidates, acq_vals = optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
return_best_only=False,
batch_initial_conditions=torch.zeros(
num_restarts, q, 3, device=self.device, dtype=dtype
),
)
self.assertTrue(torch.equal(candidates, mock_candidates))
self.assertTrue(torch.equal(acq_vals, mock_acq_values))
self.assertEqual(mock_gen_batch_initial_conditions.call_count, cnt)
cnt += 1
# test OneShotAcquisitionFunction
mock_acq_function = MockOneShotAcquisitionFunction()
candidates, acq_vals = optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
)
self.assertTrue(
torch.equal(
candidates, mock_acq_function.extract_candidates(mock_candidates[0])
)
)
self.assertTrue(torch.equal(acq_vals, mock_acq_values[0]))
def gen_value_function_initial_conditions(
acq_function: AcquisitionFunction,
bounds: Tensor,
num_restarts: int,
raw_samples: int,
current_model: Model,
options: Optional[Dict[str, Union[bool, float, int]]] = None,
) -> Tensor:
r"""Generate a batch of smart initializations for optimizing
the value function of qKnowledgeGradient.
This function generates initial conditions for optimizing the inner problem of
KG, i.e. its value function, using the maximizer of the posterior objective.
Intutively, the maximizer of the fantasized posterior will often be close to a
maximizer of the current posterior. This function uses that fact to generate the
initital conditions for the fantasy points. Specifically, a fraction of `1 -
frac_random` (see options) of raw samples is generated by sampling from the set of
maximizers of the posterior objective (obtained via random restart optimization)
according to a softmax transformation of their respective values. This means that
this initialization strategy internally solves an acquisition function
maximization problem. The remaining raw samples are generated using
`draw_sobol_samples`. All raw samples are then evaluated, and the initial
conditions are selected according to the standard initialization strategy in
'initialize_q_batch' individually for each inner problem.
Args:
acq_function: The value function instance to be optimized.
bounds: A `2 x d` tensor of lower and upper bounds for each column of
task features.
num_restarts: The number of starting points for multistart acquisition
function optimization.
raw_samples: The number of raw samples to consider in the initialization
heuristic.
current_model: The model of the KG acquisition function that was used to
generate the fantasy model of the value function.
options: Options for initial condition generation. These contain all
settings for the standard heuristic initialization from
`gen_batch_initial_conditions`. In addition, they contain
`frac_random` (the fraction of fully random fantasy points),
`num_inner_restarts` and `raw_inner_samples` (the number of random
restarts and raw samples for solving the posterior objective
maximization problem, respectively) and `eta` (temperature parameter
for sampling heuristic from posterior objective maximizers).
Returns:
A `num_restarts x batch_shape x q x d` tensor that can be used as initial
conditions for `optimize_acqf()`. Here `batch_shape` is the batch shape
of value function model.
Example:
>>> fant_X = torch.rand(5, 1, 2)
>>> fantasy_model = model.fantasize(fant_X, SobolQMCNormalSampler(16))
>>> value_function = PosteriorMean(fantasy_model)
>>> bounds = torch.tensor([[0., 0.], [1., 1.]])
>>> Xinit = gen_value_function_initial_conditions(
>>> value_function, bounds, num_restarts=10, raw_samples=512,
>>> options={"frac_random": 0.25},
>>> )
"""
options = options or {}
seed: Optional[int] = options.get("seed")
frac_random: float = options.get("frac_random", 0.6)
if not 0 < frac_random < 1:
raise ValueError(
f"frac_random must take on values in (0,1). Value: {frac_random}")
# compute maximizer of the current value function
value_function = _get_value_function(
model=current_model,
objective=acq_function.objective,
sampler=getattr(acq_function, "sampler", None),
project=getattr(acq_function, "project", None),
)
from botorch.optim.optimize import optimize_acqf
fantasy_cands, fantasy_vals = optimize_acqf(
acq_function=value_function,
bounds=bounds,
q=1,
num_restarts=options.get("num_inner_restarts", 20),
raw_samples=options.get("raw_inner_samples", 1024),
return_best_only=False,
options={
k: v
for k, v in options.items()
if k not in ("frac_random", "num_inner_restarts",
"raw_inner_samples", "eta")
},
)
batch_shape = acq_function.model.batch_shape
# sampling from the optimizers
n_value = int((1 - frac_random) * raw_samples) # number of non-random ICs
if n_value > 0:
eta = options.get("eta", 2.0)
weights = torch.exp(eta * standardize(fantasy_vals))
idx = batched_multinomial(
weights=weights.expand(*batch_shape, -1),
num_samples=n_value,
replacement=True,
).permute(-1, *range(len(batch_shape)))
resampled = fantasy_cands[idx]
else:
resampled = torch.empty(0,
*batch_shape,
1,
bounds.shape[-1],
dtype=bounds.dtype)
# add qMC samples
randomized = draw_sobol_samples(bounds=bounds,
n=raw_samples - n_value,
q=1,
batch_shape=batch_shape,
seed=seed)
# full set of raw samples
X_rnd = torch.cat([resampled, randomized], dim=0)
# evaluate the raw samples
with torch.no_grad():
Y_rnd = acq_function(X_rnd)
# select the restart points using the heuristic
return initialize_q_batch(X=X_rnd,
Y=Y_rnd,
n=num_restarts,
eta=options.get("eta", 2.0))
def alebo_acqf_optimizer(
acq_function: AcquisitionFunction,
bounds: Tensor,
n: int,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]],
fixed_features: Optional[Dict[int, float]],
rounding_func: Optional[Callable[[Tensor], Tensor]],
raw_samples: int,
num_restarts: int,
B: Tensor,
) -> Tuple[Tensor, Tensor]:
"""
Optimize the acquisition function for ALEBO.
We are optimizing over a polytope within the subspace, and so begin each
random restart of the acquisition function optimization with points that
lie within that polytope.
"""
candidate_list, acq_value_list = [], []
candidates = torch.tensor([], device=B.device, dtype=B.dtype)
try:
base_X_pending = acq_function.X_pending # pyre-ignore
acq_has_X_pend = True
except AttributeError:
base_X_pending = None
acq_has_X_pend = False
assert n == 1
for i in range(n):
# Generate initial points for optimization inside embedding
m_init = ALEBOInitializer(B.cpu().numpy(), nsamp=10 * raw_samples)
Xrnd_npy, _ = m_init.gen(n=raw_samples, bounds=[(-1.0, 1.0)] * B.shape[1])
Xrnd = torch.tensor(Xrnd_npy, dtype=B.dtype, device=B.device).unsqueeze(1)
Yrnd = torch.matmul(Xrnd, B.t()) # Project down to the embedding
with gpytorch.settings.max_cholesky_size(2000):
with torch.no_grad():
alpha = acq_function(Yrnd)
Yinit = initialize_q_batch_nonneg(X=Yrnd, Y=alpha, n=num_restarts)
# Optimize the acquisition function, separately for each random restart.
candidate, acq_value = optimize_acqf(
acq_function=acq_function,
bounds=[None, None], # pyre-ignore
q=1,
num_restarts=num_restarts,
raw_samples=0,
options={"method": "SLSQP", "batch_limit": 1},
inequality_constraints=inequality_constraints,
batch_initial_conditions=Yinit,
sequential=False,
)
candidate_list.append(candidate)
acq_value_list.append(acq_value)
candidates = torch.cat(candidate_list, dim=-2)
if acq_has_X_pend:
acq_function.set_X_pending(
torch.cat([base_X_pending, candidates], dim=-2)
if base_X_pending is not None
else candidates
)
logger.info(f"Generated sequential candidate {i+1} of {n}")
if acq_has_X_pend:
acq_function.set_X_pending(base_X_pending)
return candidates, torch.stack(acq_value_list)
def gen(
self,
n: int,
bounds: List,
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[Tensor]] = None,
model_gen_options: Optional[TConfig] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Tuple[Tensor, Tensor, TGenMetadata, List[TCandidateMetadata]]:
if linear_constraints is not None or outcome_constraints is not None:
raise UnsupportedError(
"Constraints are not yet supported by max-value entropy search!"
)
if len(objective_weights) > 1:
raise UnsupportedError(
"Models with multiple outcomes are not yet supported by MES!"
)
options = model_gen_options or {}
acf_options = options.get("acquisition_function_kwargs", {})
optimizer_options = options.get("optimizer_kwargs", {})
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.Xs,
pending_observations=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
model = self.model
# subset model only to the outcomes we need for the optimization
if options.get("subset_model", True):
model, objective_weights, outcome_constraints = subset_model(
model=model, # pyre-ignore [6]
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
# get the acquisition function
num_fantasies = acf_options.get("num_fantasies", 16)
num_mv_samples = acf_options.get("num_mv_samples", 10)
num_y_samples = acf_options.get("num_y_samples", 128)
candidate_size = acf_options.get("candidate_size", 1000)
num_restarts = optimizer_options.get("num_restarts", 40)
raw_samples = optimizer_options.get("raw_samples", 1024)
# generate the discrete points in the design space to sample max values
bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device)
bounds_ = bounds_.transpose(0, 1)
candidate_set = torch.rand(candidate_size, bounds_.size(1))
candidate_set = bounds_[0] + (bounds_[1] - bounds_[0]) * candidate_set
acq_function = _instantiate_MES(
model=model, # pyre-ignore [6]
candidate_set=candidate_set,
num_fantasies=num_fantasies,
num_trace_observations=options.get("num_trace_observations", 0),
num_mv_samples=num_mv_samples,
num_y_samples=num_y_samples,
X_pending=X_pending,
maximize=True if objective_weights[0] == 1 else False,
target_fidelities=target_fidelities,
fidelity_weights=options.get("fidelity_weights"),
cost_intercept=self.cost_intercept,
)
# optimize and get new points
botorch_rounding_func = get_rounding_func(rounding_func)
candidates, _ = optimize_acqf(
acq_function=acq_function,
bounds=bounds_,
q=n,
inequality_constraints=None,
fixed_features=fixed_features,
post_processing_func=botorch_rounding_func,
num_restarts=num_restarts,
raw_samples=raw_samples,
options={
"batch_limit": optimizer_options.get("batch_limit", 8),
"maxiter": optimizer_options.get("maxiter", 200),
"method": "L-BFGS-B",
"nonnegative": optimizer_options.get("nonnegative", False),
},
sequential=True,
)
new_x = candidates.detach().cpu()
# pyre-fixme[7]: Expected `Tuple[Tensor, Tensor, Dict[str, typing.Any],
# List[Optional[Dict[str, typing.Any]]]]` but got `Tuple[Tensor, typing.Any,
# Dict[str, typing.Any], None]`.
return new_x, torch.ones(n, dtype=self.dtype), {}, None
def gen_one_shot_kg_initial_conditions(
acq_function: qKnowledgeGradient,
bounds: Tensor,
q: int,
num_restarts: int,
raw_samples: int,
options: Optional[Dict[str, Union[bool, float, int]]] = None,
) -> Optional[Tensor]:
r"""Generate a batch of smart initializations for qKnowledgeGradient.
This function generates initial conditions for optimizing one-shot KG using
the maximizer of the posterior objective. Intutively, the maximizer of the
fantasized posterior will often be close to a maximizer of the current
posterior. This function uses that fact to generate the initital conditions
for the fantasy points. Specifically, a fraction of `1 - frac_random` (see
options) is generated by sampling from the set of maximizers of the
posterior objective (obtained via random restart optimization) according to
a softmax transformation of their respective values. This means that this
initialization strategy internally solves an acquisition function
maximization problem. The remaining `frac_random` fantasy points as well as
all `q` candidate points are chosen according to the standard initialization
strategy in `gen_batch_initial_conditions`.
Args:
acq_function: The qKnowledgeGradient instance to be optimized.
bounds: A `2 x d` tensor of lower and upper bounds for each column of
task features.
q: The number of candidates to consider.
num_restarts: The number of starting points for multistart acquisition
function optimization.
raw_samples: The number of raw samples to consider in the initialization
heuristic.
options: Options for initial condition generation. These contain all
settings for the standard heuristic initialization from
`gen_batch_initial_conditions`. In addition, they contain
`frac_random` (the fraction of fully random fantasy points),
`num_inner_restarts` and `raw_inner_samples` (the number of random
restarts and raw samples for solving the posterior objective
maximization problem, respectively) and `eta` (temperature parameter
for sampling heuristic from posterior objective maximizers).
Returns:
A `num_restarts x q' x d` tensor that can be used as initial conditions
for `optimize_acqf()`. Here `q' = q + num_fantasies` is the total number
of points (candidate points plus fantasy points).
Example:
>>> qKG = qKnowledgeGradient(model, num_fantasies=64)
>>> bounds = torch.tensor([[0., 0.], [1., 1.]])
>>> Xinit = gen_one_shot_kg_initial_conditions(
>>> qKG, bounds, q=3, num_restarts=10, raw_samples=512,
>>> options={"frac_random": 0.25},
>>> )
"""
options = options or {}
frac_random: float = options.get("frac_random", 0.1)
if not 0 < frac_random < 1:
raise ValueError(
f"frac_random must take on values in (0,1). Value: {frac_random}")
q_aug = acq_function.get_augmented_q_batch_size(q=q)
# TODO: Avoid unnecessary computation by not generating all candidates
ics = gen_batch_initial_conditions(
acq_function=acq_function,
bounds=bounds,
q=q_aug,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
)
# compute maximizer of the value function
value_function = _get_value_function(
model=acq_function.model,
objective=acq_function.objective,
sampler=acq_function.inner_sampler,
project=getattr(acq_function, "project", None),
)
from botorch.optim.optimize import optimize_acqf
fantasy_cands, fantasy_vals = optimize_acqf(
acq_function=value_function,
bounds=bounds,
q=1,
num_restarts=options.get("num_inner_restarts", 20),
raw_samples=options.get("raw_inner_samples", 1024),
return_best_only=False,
)
# sampling from the optimizers
n_value = int((1 - frac_random) * (q_aug - q)) # number of non-random ICs
eta = options.get("eta", 2.0)
weights = torch.exp(eta * standardize(fantasy_vals))
idx = torch.multinomial(weights, num_restarts * n_value, replacement=True)
# set the respective initial conditions to the sampled optimizers
ics[..., -n_value:, :] = fantasy_cands[idx,
0].view(num_restarts, n_value, -1)
return ics
def optimize(
self,
n: int,
search_space_digest: SearchSpaceDigest,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor,
float]]] = None,
fixed_features: Optional[Dict[int, float]] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
optimizer_options: Optional[Dict[str, Any]] = None,
) -> Tuple[Tensor, Tensor]:
"""Generate a set of candidates via multi-start optimization. Obtains
candidates and their associated acquisition function values.
Args:
n: The number of candidates to generate.
search_space_digest: A ``SearchSpaceDigest`` object containing search space
properties, e.g. ``bounds`` for optimization.
inequality constraints: A list of tuples (indices, coefficients, rhs),
with each tuple encoding an inequality constraint of the form
``sum_i (X[indices[i]] * coefficients[i]) >= rhs``.
fixed_features: A map `{feature_index: value}` for features that
should be fixed to a particular value during generation.
rounding_func: A function that post-processes an optimization
result appropriately (i.e., according to `round-trip`
transformations).
optimizer_options: Options for the optimizer function, e.g. ``sequential``
or ``raw_samples``.
"""
# NOTE: Could make use of `optimizer_class` when its added to BoTorch
# instead of calling `optimizer_acqf` or `optimize_acqf_discrete` etc.
optimizer_options_with_defaults = {
**get_default_optimizer_options(acqf_class=self.botorch_acqf_class),
**(optimizer_options or {}),
}
ssd = search_space_digest
tkwargs = {"dtype": self.dtype, "device": self.device}
# pyre-fixme[6]: Expected `Optional[Type[torch._dtype]]` for 2nd param but
# got `Union[Type[torch._dtype], torch.device]`.
bounds = torch.tensor(ssd.bounds, **tkwargs).transpose(0, 1)
discrete_features = sorted(ssd.ordinal_features +
ssd.categorical_features)
if fixed_features is not None:
for i in fixed_features:
if not 0 <= i < len(ssd.feature_names):
raise ValueError(f"Invalid fixed_feature index: {i}")
# 1. Handle the fully continuous search space.
if not discrete_features:
return optimize_acqf(
acq_function=self.acqf,
bounds=bounds,
q=n,
inequality_constraints=inequality_constraints,
fixed_features=fixed_features,
post_processing_func=rounding_func,
**optimizer_options_with_defaults,
)
# 2. Handle search spaces with discrete features.
discrete_choices = mk_discrete_choices(ssd=ssd,
fixed_features=fixed_features)
# 2a. Handle the fully discrete search space.
if len(discrete_choices) == len(ssd.feature_names):
# For now we just enumerate all possible discrete combinations. This is not
# scalable and and only works for a reasonably small number of choices.
all_choices = (discrete_choices[i]
for i in range(len(discrete_choices)))
# pyre-fixme[6]: Expected `Optional[Type[torch._dtype]]` for 2nd param
# but got `Union[Type[torch._dtype], torch.device]`.
all_choices = torch.tensor(list(itertools.product(*all_choices)),
**tkwargs)
return optimize_acqf_discrete(
acq_function=self.acqf,
q=n,
choices=all_choices,
**optimizer_options_with_defaults,
)
# 2b. Handle mixed search spaces that have discrete and continuous features.
return optimize_acqf_mixed(
acq_function=self.acqf,
bounds=bounds,
q=n,
# For now we just enumerate all possible discrete combinations. This is not
# scalable and and only works for a reasonably small number of choices. A
# slowdown warning is logged in `enumerate_discrete_combinations` if needed.
fixed_features_list=enumerate_discrete_combinations(
discrete_choices=discrete_choices),
inequality_constraints=inequality_constraints,
post_processing_func=rounding_func,
**optimizer_options_with_defaults,
)
def test_optimize_acqf_joint(self, mock_gen_candidates,
mock_gen_batch_initial_conditions):
q = 3
num_restarts = 2
raw_samples = 10
options = {}
mock_acq_function = MockAcquisitionFunction()
cnt = 0
for dtype in (torch.float, torch.double):
mock_gen_batch_initial_conditions.return_value = torch.zeros(
num_restarts, q, 3, device=self.device, dtype=dtype)
base_cand = torch.arange(3, device=self.device,
dtype=dtype).expand(1, q, 3)
mock_candidates = torch.cat(
[i * base_cand for i in range(num_restarts)], dim=0)
mock_acq_values = num_restarts - torch.arange(
num_restarts, device=self.device, dtype=dtype)
mock_gen_candidates.return_value = (mock_candidates,
mock_acq_values)
bounds = torch.stack([
torch.zeros(3, device=self.device, dtype=dtype),
4 * torch.ones(3, device=self.device, dtype=dtype),
])
candidates, acq_vals = optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
)
self.assertTrue(torch.equal(candidates, mock_candidates[0]))
self.assertTrue(torch.equal(acq_vals, mock_acq_values[0]))
cnt += 1
self.assertEqual(mock_gen_batch_initial_conditions.call_count, cnt)
# test generation with provided initial conditions
candidates, acq_vals = optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
return_best_only=False,
batch_initial_conditions=torch.zeros(num_restarts,
q,
3,
device=self.device,
dtype=dtype),
)
self.assertTrue(torch.equal(candidates, mock_candidates))
self.assertTrue(torch.equal(acq_vals, mock_acq_values))
self.assertEqual(mock_gen_batch_initial_conditions.call_count, cnt)
# test fixed features
fixed_features = {0: 0.1}
mock_candidates[:, 0] = 0.1
mock_gen_candidates.return_value = (mock_candidates,
mock_acq_values)
candidates, acq_vals = optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
fixed_features=fixed_features,
)
self.assertEqual(
mock_gen_candidates.call_args[1]["fixed_features"],
fixed_features)
self.assertTrue(torch.equal(candidates, mock_candidates[0]))
cnt += 1
self.assertEqual(mock_gen_batch_initial_conditions.call_count, cnt)
# test trivial case when all features are fixed
candidates, acq_vals = optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
fixed_features={
0: 0.1,
1: 0.2,
2: 0.3
},
)
self.assertTrue(
torch.equal(
candidates,
torch.tensor([0.1, 0.2, 0.3],
device=self.device,
dtype=dtype).expand(3, 3),
))
self.assertEqual(mock_gen_batch_initial_conditions.call_count, cnt)
# test OneShotAcquisitionFunction
mock_acq_function = MockOneShotAcquisitionFunction()
candidates, acq_vals = optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
)
self.assertTrue(
torch.equal(
candidates,
mock_acq_function.extract_candidates(mock_candidates[0])))
self.assertTrue(torch.equal(acq_vals, mock_acq_values[0]))
def test_optimize_acqf_nonlinear_constraints(self):
num_restarts = 2
for dtype in (torch.float, torch.double):
tkwargs = {"device": self.device, "dtype": dtype}
mock_acq_function = SquaredAcquisitionFunction()
bounds = torch.stack(
[torch.zeros(3, **tkwargs), 4 * torch.ones(3, **tkwargs)]
)
# Make sure we find the global optimum [4, 4, 4] without constraints
with torch.random.fork_rng():
torch.manual_seed(0)
candidates, acq_value = optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=1,
num_restarts=num_restarts,
sequential=True,
raw_samples=16,
)
self.assertTrue(torch.allclose(candidates, 4 * torch.ones(3, **tkwargs)))
# Constrain the sum to be <= 4 in which case the solution is a
# permutation of [4, 0, 0]
def nlc1(x):
return 4 - x.sum(dim=-1)
batch_initial_conditions = torch.tensor([[[0.5, 0.5, 3]]], **tkwargs)
candidates, acq_value = optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=1,
nonlinear_inequality_constraints=[nlc1],
batch_initial_conditions=batch_initial_conditions,
num_restarts=1,
)
self.assertTrue(
torch.allclose(
torch.sort(candidates).values,
torch.tensor([[0, 0, 4]], **tkwargs),
)
)
self.assertTrue(
torch.allclose(acq_value, torch.tensor([4], **tkwargs), atol=1e-3)
)
# Make sure we return the initial solution if SLSQP fails to return
# a feasible point.
with mock.patch("botorch.generation.gen.minimize") as mock_minimize:
mock_minimize.return_value = OptimizeResult(x=np.array([4, 4, 4]))
candidates, acq_value = optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=1,
nonlinear_inequality_constraints=[nlc1],
batch_initial_conditions=batch_initial_conditions,
num_restarts=1,
)
self.assertTrue(torch.allclose(candidates, batch_initial_conditions))
# Constrain all variables to be >= 1. The global optimum is 2.45 and
# is attained by some permutation of [1, 1, 2]
def nlc2(x):
return x[..., 0] - 1
def nlc3(x):
return x[..., 1] - 1
def nlc4(x):
return x[..., 2] - 1
with torch.random.fork_rng():
torch.manual_seed(0)
batch_initial_conditions = 1 + 0.33 * torch.rand(
num_restarts, 1, 3, **tkwargs
)
candidates, acq_value = optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=1,
nonlinear_inequality_constraints=[nlc1, nlc2, nlc3, nlc4],
batch_initial_conditions=batch_initial_conditions,
num_restarts=num_restarts,
)
self.assertTrue(
torch.allclose(
torch.sort(candidates).values,
torch.tensor([[1, 1, 2]], **tkwargs),
)
)
self.assertTrue(
torch.allclose(acq_value, torch.tensor(2.45, **tkwargs), atol=1e-3)
)
# Make sure fixed features aren't supported
with self.assertRaisesRegex(
NotImplementedError,
"Fixed features are not supported when non-linear inequality "
"constraints are given.",
):
optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=1,
nonlinear_inequality_constraints=[nlc1, nlc2, nlc3, nlc4],
batch_initial_conditions=batch_initial_conditions,
num_restarts=num_restarts,
fixed_features={0: 0.1},
)
# Constraints must be passed in as lists
with self.assertRaisesRegex(
ValueError,
"`nonlinear_inequality_constraints` must be a list of callables, "
"got <class 'function'>.",
):
optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=1,
nonlinear_inequality_constraints=nlc1,
num_restarts=num_restarts,
batch_initial_conditions=batch_initial_conditions,
)
# batch_initial_conditions must be given
with self.assertRaisesRegex(
NotImplementedError,
"`batch_initial_conditions` must be given if there are non-linear "
"inequality constraints.",
):
optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=1,
nonlinear_inequality_constraints=[nlc1],
num_restarts=num_restarts,
)
# batch_initial_conditions must be feasible
with self.assertRaisesRegex(
ValueError,
"`batch_initial_conditions` must satisfy the non-linear "
"inequality constraints.",
):
optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=1,
nonlinear_inequality_constraints=[nlc1],
num_restarts=num_restarts,
batch_initial_conditions=4 * torch.ones(1, 1, 3, **tkwargs),
)
# Explicitly setting batch_limit to be >1 should raise
with self.assertRaisesRegex(
ValueError,
"`batch_limit` must be 1 when non-linear inequality constraints "
"are given.",
):
optimize_acqf(
acq_function=mock_acq_function,
bounds=bounds,
q=1,
nonlinear_inequality_constraints=[nlc1],
batch_initial_conditions=torch.rand(5, 1, 3, **tkwargs),
num_restarts=5,
options={"batch_limit": 5},
)
def best_point(
self,
bounds: List[Tuple[float, float]],
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
model_gen_options: Optional[TConfig] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Optional[Tensor]:
"""
Identify the current best point, satisfying the constraints in the same
format as to gen.
Return None if no such point can be identified.
Args:
bounds: A list of (lower, upper) tuples for each column of X.
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
outcome_constraints: A tuple of (A, b). For k outcome constraints
and m outputs at f(x), A is (k x m) and b is (k x 1) such that
A f(x) <= b.
linear_constraints: A tuple of (A, b). For k linear constraints on
d-dimensional x, A is (k x d) and b is (k x 1) such that
A x <= b.
fixed_features: A map {feature_index: value} for features that
should be fixed to a particular value in the best point.
model_gen_options: A config dictionary that can contain
model-specific options.
target_fidelities: A map {feature_index: value} of fidelity feature
column indices to their respective target fidelities. Used for
multi-fidelity optimization.
Returns:
d-tensor of the best point.
"""
if linear_constraints is not None or outcome_constraints is not None:
raise UnsupportedError(
"Constraints are not yet supported by max-value entropy search!"
)
options = model_gen_options or {}
fixed_features = fixed_features or {}
optimizer_options = options.get("optimizer_kwargs", {})
X_observed = get_observed(
Xs=self.Xs,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
acq_function, non_fixed_idcs = self._get_best_point_acqf(
X_observed=X_observed, # pyre-ignore: [6]
objective_weights=objective_weights,
fixed_features=fixed_features,
target_fidelities=target_fidelities,
)
return_best_only = optimizer_options.get("return_best_only", True)
bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device)
bounds_ = bounds_.transpose(0, 1)
if non_fixed_idcs is not None:
bounds_ = bounds_[..., non_fixed_idcs]
candidates, _ = optimize_acqf(
acq_function=acq_function,
bounds=bounds_,
q=1,
num_restarts=optimizer_options.get("num_restarts", 60),
raw_samples=optimizer_options.get("raw_samples", 1024),
inequality_constraints=None,
fixed_features=None, # handled inside the acquisition function
options={
"batch_limit": optimizer_options.get("batch_limit", 8),
"maxiter": optimizer_options.get("maxiter", 200),
"nonnegative": optimizer_options.get("nonnegative", False),
"method": "L-BFGS-B",
},
return_best_only=return_best_only,
)
rec_point = candidates.detach().cpu()
if isinstance(acq_function, FixedFeatureAcquisitionFunction):
rec_point = acq_function._construct_X_full(rec_point)
if return_best_only:
rec_point = rec_point.view(-1)
return rec_point
def optimize_objective(
model: Model,
bounds: Tensor,
q: int,
objective: Optional[MCAcquisitionObjective] = None,
posterior_transform: Optional[PosteriorTransform] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
qmc: bool = True,
mc_samples: int = 512,
seed_inner: Optional[int] = None,
optimizer_options: Dict[str, Any] = None,
post_processing_func: Optional[Callable[[Tensor], Tensor]] = None,
batch_initial_conditions: Optional[Tensor] = None,
sequential: bool = False,
**ignore,
) -> Tuple[Tensor, Tensor]:
r"""Optimize an objective under the given model.
Args:
model: The model to be used in the objective.
bounds: A `2 x d` tensor of lower and upper bounds for each column of `X`.
q: The cardinality of input sets on which the objective is to be evaluated.
objective: The objective to optimize.
posterior_transform: The posterior transform to be used in the
acquisition function.
linear_constraints: A tuple of (A, b). Given `k` linear constraints on a
`d`-dimensional space, `A` is `k x d` and `b` is `k x 1` such that
`A x <= b`. (Not used by single task models).
fixed_features: A dictionary of feature assignments `{feature_index: value}` to
hold fixed during generation.
target_fidelities: A dictionary mapping input feature indices to fidelity
values. Defaults to `{-1: 1.0}`.
qmc: Toggle for enabling (qmc=1) or disabling (qmc=0) use of Quasi Monte Carlo.
mc_samples: Integer number of samples used to estimate Monte Carlo objectives.
seed_inner: Integer seed used to initialize the sampler passed to MCObjective.
optimizer_options: Table used to lookup keyword arguments for the optimizer.
post_processing_func: A function that post-processes an optimization
result appropriately (i.e. according to `round-trip` transformations).
batch_initial_conditions: A Tensor of initial values for the optimizer.
sequential: If False, uses joint optimization, otherwise uses sequential
optimization.
Returns:
A tuple containing the best input locations and corresponding objective values.
"""
if optimizer_options is None:
optimizer_options = {}
if objective is not None:
sampler_cls = SobolQMCNormalSampler if qmc else IIDNormalSampler
acq_function = qSimpleRegret(
model=model,
objective=objective,
posterior_transform=posterior_transform,
sampler=sampler_cls(num_samples=mc_samples, seed=seed_inner),
)
else:
acq_function = PosteriorMean(model=model,
posterior_transform=posterior_transform)
if fixed_features:
acq_function = FixedFeatureAcquisitionFunction(
acq_function=acq_function,
d=bounds.shape[-1],
columns=list(fixed_features.keys()),
values=list(fixed_features.values()),
)
free_feature_dims = list(range(len(bounds)) - fixed_features.keys())
free_feature_bounds = bounds[:, free_feature_dims] # (2, d' <= d)
else:
free_feature_bounds = bounds
if linear_constraints is None:
inequality_constraints = None
else:
A, b = linear_constraints
inequality_constraints = []
k, d = A.shape
for i in range(k):
indicies = A[i, :].nonzero(as_tuple=False).squeeze()
coefficients = -A[i, indicies]
rhs = -b[i, 0]
inequality_constraints.append((indicies, coefficients, rhs))
return optimize_acqf(
acq_function=acq_function,
bounds=free_feature_bounds,
q=q,
num_restarts=optimizer_options.get("num_restarts", 60),
raw_samples=optimizer_options.get("raw_samples", 1024),
options={
"batch_limit": optimizer_options.get("batch_limit", 8),
"maxiter": optimizer_options.get("maxiter", 200),
"nonnegative": optimizer_options.get("nonnegative", False),
"method": optimizer_options.get("method", "L-BFGS-B"),
},
inequality_constraints=inequality_constraints,
fixed_features=None, # handled inside the acquisition function
post_processing_func=post_processing_func,
batch_initial_conditions=batch_initial_conditions,
return_best_only=True,
sequential=sequential,
)
def scipy_optimizer(
acq_function: AcquisitionFunction,
bounds: Tensor,
n: int,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
fixed_features: Optional[Dict[int, float]] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
**kwargs: Any,
) -> Tuple[Tensor, Tensor]:
r"""Optimizer using scipy's minimize module on a numpy-adpator.
Args:
acq_function: A botorch AcquisitionFunction.
bounds: A `2 x d`-dim tensor, where `bounds[0]` (`bounds[1]`) are the
lower (upper) bounds of the feasible hyperrectangle.
n: The number of candidates to generate.
inequality constraints: A list of tuples (indices, coefficients, rhs),
with each tuple encoding an inequality constraint of the form
`\sum_i (X[indices[i]] * coefficients[i]) >= rhs`
equality constraints: A list of tuples (indices, coefficients, rhs),
with each tuple encoding an equality constraint of the form
`\sum_i (X[indices[i]] * coefficients[i]) == rhs`
fixed_features: A map {feature_index: value} for features that should
be fixed to a particular value during generation.
rounding_func: A function that rounds an optimization result
appropriately (i.e., according to `round-trip` transformations).
Returns:
2-element tuple containing
- A `n x d`-dim tensor of generated candidates.
- In the case of joint optimization, a scalar tensor containing
the joint acquisition value of the `n` points. In the case of
sequential optimization, a `n`-dim tensor of conditional acquisition
values, where `i`-th element is the expected acquisition value
conditional on having observed candidates `0,1,...,i-1`.
"""
num_restarts: int = kwargs.get("num_restarts", 20)
raw_samples: int = kwargs.get("num_raw_samples", 50 * num_restarts)
if kwargs.get("joint_optimization", False):
sequential = False
else:
sequential = True
# use SLSQP by default for small problems since it yields faster wall times
if "method" not in kwargs:
kwargs["method"] = "SLSQP"
X, expected_acquisition_value = optimize_acqf(
acq_function=acq_function,
bounds=bounds,
q=n,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=kwargs,
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
fixed_features=fixed_features,
sequential=sequential,
post_processing_func=rounding_func,
)
return X, expected_acquisition_value
def gen(
self,
n: int,
bounds: List,
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[Tensor]] = None,
model_gen_options: Optional[TConfig] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Tuple[Tensor, Tensor, TGenMetadata]:
"""
Generate new candidates.
Args:
n: Number of candidates to generate.
bounds: A list of (lower, upper) tuples for each column of X.
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
outcome_constraints: A tuple of (A, b). For k outcome constraints
and m outputs at f(x), A is (k x m) and b is (k x 1) such that
A f(x) <= b.
linear_constraints: A tuple of (A, b). For k linear constraints on
d-dimensional x, A is (k x d) and b is (k x 1) such that
A x <= b.
fixed_features: A map {feature_index: value} for features that
should be fixed to a particular value during generation.
pending_observations: A list of m (k_i x d) feature tensors X
for m outcomes and k_i pending observations for outcome i.
model_gen_options: A config dictionary that can contain
model-specific options.
rounding_func: A function that rounds an optimization result
appropriately (i.e., according to `round-trip` transformations).
target_fidelities: A map {feature_index: value} of fidelity feature
column indices to their respective target fidelities. Used for
multi-fidelity optimization.
Returns:
3-element tuple containing
- (n x d) tensor of generated points.
- n-tensor of weights for each point.
- Dictionary of model-specific metadata for the given
generation candidates.
"""
options = model_gen_options or {}
acf_options = options.get("acquisition_function_kwargs", {})
optimizer_options = options.get("optimizer_kwargs", {})
X_pending, X_observed = _get_X_pending_and_observed(
Xs=self.Xs,
pending_observations=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
model = self.model
# subset model only to the outcomes we need for the optimization
if options.get("subset_model", True):
model, objective_weights, outcome_constraints = subset_model(
model=model, # pyre-ignore [6]
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
objective = _get_objective(
model=model, # pyre-ignore [6]
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
)
# get the acquisition function
n_fantasies = acf_options.get("num_fantasies", 64)
qmc = acf_options.get("qmc", True)
seed_inner = acf_options.get("seed_inner", None)
num_restarts = optimizer_options.get("num_restarts", 40)
raw_samples = optimizer_options.get("raw_samples", 1024)
inequality_constraints = _to_inequality_constraints(linear_constraints)
# TODO: update optimizers to handle inequality_constraints
if inequality_constraints is not None:
raise UnsupportedError(
"Inequality constraints are not yet supported for KnowledgeGradient!"
)
# get current value
best_point_acqf, non_fixed_idcs = self._get_best_point_acqf(
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed, # pyre-ignore: [6]
seed_inner=seed_inner,
fixed_features=fixed_features,
target_fidelities=target_fidelities,
qmc=qmc,
)
# solution from previous iteration
recommended_point = self.best_point(
bounds=bounds,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
model_gen_options=model_gen_options,
target_fidelities=target_fidelities,
)
recommended_point = recommended_point.detach().unsqueeze(
0) # pyre-ignore: [16]
# Extract acquisition value (TODO: Make this less painful and repetitive)
if non_fixed_idcs is not None:
recommended_point = recommended_point[..., non_fixed_idcs]
current_value = best_point_acqf(recommended_point).max()
acq_function = _instantiate_KG(
model=model, # pyre-ignore [6]
objective=objective,
qmc=qmc,
n_fantasies=n_fantasies,
num_trace_observations=options.get("num_trace_observations", 0),
mc_samples=acf_options.get("mc_samples", 256),
seed_inner=seed_inner,
seed_outer=acf_options.get("seed_outer", None),
X_pending=X_pending,
target_fidelities=target_fidelities,
fidelity_weights=options.get("fidelity_weights"),
current_value=current_value,
cost_intercept=self.cost_intercept,
)
# optimize and get new points
bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device)
bounds_ = bounds_.transpose(0, 1)
batch_initial_conditions = gen_one_shot_kg_initial_conditions(
acq_function=acq_function,
bounds=bounds_,
q=n,
num_restarts=num_restarts,
raw_samples=raw_samples,
options={
"frac_random": optimizer_options.get("frac_random", 0.1),
"num_inner_restarts": num_restarts,
"raw_inner_samples": raw_samples,
},
)
botorch_rounding_func = get_rounding_func(rounding_func)
candidates, _ = optimize_acqf(
acq_function=acq_function,
bounds=bounds_,
q=n,
inequality_constraints=inequality_constraints,
fixed_features=fixed_features,
post_processing_func=botorch_rounding_func,
num_restarts=num_restarts,
raw_samples=raw_samples,
options={
"batch_limit": optimizer_options.get("batch_limit", 8),
"maxiter": optimizer_options.get("maxiter", 200),
"method": "L-BFGS-B",
"nonnegative": optimizer_options.get("nonnegative", False),
},
batch_initial_conditions=batch_initial_conditions,
)
new_x = candidates.detach().cpu()
return new_x, torch.ones(n, dtype=self.dtype), {}
